Description

solves recursively on n (p being fixed)
the following system (normal equations), i.e. identifies
the AR part (poles) of a vector ARMA(n,p) process,

where {Rk;k=1,nlag} is the sequence of empirical covariances.

Example

//Generate the processt1=0:0.1:100;y1=sin(2*%pi*t1)+sin(2*%pi*2*t1);y1=y1+rand(y1,"normal");//Covariance of y1nlag=128;c1=corr(y1,nlag);c1=c1';//Compute the filter with maximum order=15 and p=1n=15;[la1,sig1]=lattn(n,1,c1);//Compare result of poles with p=-1 and with levin function[la2,sig2]=lattn(n,-1,c1);fori=1:ns2=roots(la2(i));s2=log(s2)/2/%pi/.1;//estimated poless2=gsort(imag(s2));s2=s2(1:i/2);end;[la3,sig3]=levin(n,c1);fori=1:ns3=roots(la3(i));s3=log(s3)/2/%pi/.1;//estimated poless3=gsort(imag(s3));s3=s3(1:i/2);end;